--------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/alexweisiger/Documents/research/Keren/Dataverse/results.log
  log type:  text
 opened on:  19 Jan 2014, 14:14:06

. 
. use repdata, clear

. 
. ** Table 1, Model 1
. 
. probit mzmidl backdown1 capratio capsum demlo atopally cntgdumy bothmajr onema
> jor t t2 t3, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -17417.987  
Iteration 1:   log pseudolikelihood = -13584.216  
Iteration 2:   log pseudolikelihood = -12916.941  
Iteration 3:   log pseudolikelihood = -12895.253  
Iteration 4:   log pseudolikelihood =  -12895.18  
Iteration 5:   log pseudolikelihood =  -12895.18  

Probit regression                                 Number of obs   =    1187663
                                                  Wald chi2(11)   =    2821.47
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood =  -12895.18                 Pseudo R2       =     0.2597

                             (Std. Err. adjusted for 33255 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   backdown1 |   .2626327    .052726     4.98   0.000     .1592918    .3659737
    capratio |  -.5446489   .0839089    -6.49   0.000    -.7091074   -.3801904
      capsum |   3.079177   .2137162    14.41   0.000     2.660301    3.498053
       demlo |  -.0073713    .001972    -3.74   0.000    -.0112363   -.0035062
    atopally |   .0931881     .03237     2.88   0.004     .0297441    .1566321
    cntgdumy |   1.119112   .0360075    31.08   0.000     1.048538    1.189685
    bothmajr |   -.243366   .1120088    -2.17   0.030    -.4628992   -.0238328
    onemajor |   .2795616   .0408367     6.85   0.000     .1995233       .3596
           t |  -.0403499   .0027613   -14.61   0.000     -.045762   -.0349378
          t2 |   .0006278   .0000649     9.67   0.000     .0005006     .000755
          t3 |  -2.60e-06   3.83e-07    -6.79   0.000    -3.35e-06   -1.85e-06
       _cons |  -2.507946     .07444   -33.69   0.000    -2.653846   -2.362047
------------------------------------------------------------------------------

. 
. ** Model 2
. 
. probit mzmidl backdown1 midsin10 capratio capsum demlo atopally cntgdumy bothm
> ajr onemajor t t2 t3, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -17417.987  
Iteration 1:   log pseudolikelihood = -13526.396  
Iteration 2:   log pseudolikelihood = -12831.406  
Iteration 3:   log pseudolikelihood =  -12804.65  
Iteration 4:   log pseudolikelihood = -12804.549  
Iteration 5:   log pseudolikelihood = -12804.549  

Probit regression                                 Number of obs   =    1187663
                                                  Wald chi2(12)   =    2822.73
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -12804.549                 Pseudo R2       =     0.2649

                             (Std. Err. adjusted for 33255 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   backdown1 |   .1965937   .0556578     3.53   0.000     .0875063    .3056811
    midsin10 |   .0098363   .0011075     8.88   0.000     .0076657    .0120069
    capratio |  -.5741762   .0844788    -6.80   0.000    -.7397516   -.4086008
      capsum |   2.815476   .2174824    12.95   0.000     2.389218    3.241733
       demlo |  -.0066511   .0019487    -3.41   0.001    -.0104705   -.0028316
    atopally |    .080123    .031876     2.51   0.012     .0176472    .1425988
    cntgdumy |    1.11962   .0353688    31.66   0.000     1.050298    1.188941
    bothmajr |  -.2233379   .1078038    -2.07   0.038    -.4346295   -.0120462
    onemajor |   .2555515   .0408026     6.26   0.000     .1755798    .3355232
           t |  -.0400719   .0027613   -14.51   0.000     -.045484   -.0346597
          t2 |   .0006219   .0000654     9.51   0.000     .0004937    .0007502
          t3 |  -2.58e-06   3.87e-07    -6.67   0.000    -3.34e-06   -1.82e-06
       _cons |  -2.518771     .07445   -33.83   0.000     -2.66469   -2.372851
------------------------------------------------------------------------------

. 
. ** Model 3
. 
. probit mzmidl backdown1 capratio capsum demlo atopally cntgdumy bothmajr onema
> jor t t2 t3 if inmid10==1, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -15194.502  
Iteration 1:   log pseudolikelihood = -14848.051  
Iteration 2:   log pseudolikelihood = -11233.947  
Iteration 3:   log pseudolikelihood = -11197.924  
Iteration 4:   log pseudolikelihood = -11197.758  
Iteration 5:   log pseudolikelihood = -11197.758  

Probit regression                                 Number of obs   =     840670
                                                  Wald chi2(11)   =    2508.31
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -11197.758                 Pseudo R2       =     0.2630

                             (Std. Err. adjusted for 29479 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   backdown1 |   .1735308   .0522616     3.32   0.001     .0710999    .2759617
    capratio |  -.5105489   .0895995    -5.70   0.000    -.6861607   -.3349371
      capsum |    3.03389   .2112459    14.36   0.000     2.619856    3.447925
       demlo |  -.0083637   .0021388    -3.91   0.000    -.0125556   -.0041718
    atopally |   .0937015   .0334222     2.80   0.005     .0281952    .1592078
    cntgdumy |   1.098894   .0373015    29.46   0.000     1.025784    1.172004
    bothmajr |  -.2332791   .1076442    -2.17   0.030    -.4442578   -.0223004
    onemajor |   .2643625   .0412265     6.41   0.000     .1835599     .345165
           t |  -.0457317   .0032418   -14.11   0.000    -.0520855   -.0393778
          t2 |   .0007086   .0000775     9.14   0.000     .0005566    .0008605
          t3 |  -2.94e-06   4.59e-07    -6.39   0.000    -3.84e-06   -2.04e-06
       _cons |  -2.399503   .0795262   -30.17   0.000    -2.555371   -2.243634
------------------------------------------------------------------------------

. 
. ** Model 4
. 
. probit mzmidl bkdown1 midsin10 capratio capsum demlo atopally cntgdumy bothmaj
> r onemajor t t2 t3, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -17417.987  
Iteration 1:   log pseudolikelihood = -13527.171  
Iteration 2:   log pseudolikelihood = -12832.283  
Iteration 3:   log pseudolikelihood = -12805.861  
Iteration 4:   log pseudolikelihood = -12805.763  
Iteration 5:   log pseudolikelihood = -12805.763  

Probit regression                                 Number of obs   =    1187663
                                                  Wald chi2(12)   =    2875.18
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -12805.763                 Pseudo R2       =     0.2648

                             (Std. Err. adjusted for 33255 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     bkdown1 |   .2037982    .047204     4.32   0.000     .1112802    .2963163
    midsin10 |   .0091152   .0011441     7.97   0.000     .0068728    .0113576
    capratio |  -.5888107   .0855573    -6.88   0.000    -.7564999   -.4211214
      capsum |   2.831883   .2186997    12.95   0.000     2.403239    3.260526
       demlo |   -.006154   .0019351    -3.18   0.001    -.0099467   -.0023613
    atopally |    .068902   .0317118     2.17   0.030      .006748    .1310561
    cntgdumy |   1.125265   .0353916    31.79   0.000     1.055898    1.194631
    bothmajr |  -.2166895   .1085607    -2.00   0.046    -.4294646   -.0039144
    onemajor |   .2660987   .0409861     6.49   0.000     .1857674      .34643
           t |  -.0400692    .002767   -14.48   0.000    -.0454923    -.034646
          t2 |   .0006223   .0000654     9.51   0.000     .0004941    .0007505
          t3 |  -2.58e-06   3.87e-07    -6.67   0.000    -3.34e-06   -1.82e-06
       _cons |  -2.503944   .0758421   -33.02   0.000    -2.652592   -2.355296
------------------------------------------------------------------------------

. 
. ** Model 5
. 
. probit mzmidl genrep midsin10 capratio capsum demlo atopally cntgdumy bothmajr
>  onemajor t t2 t3, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -17417.987  
Iteration 1:   log pseudolikelihood = -13533.742  
Iteration 2:   log pseudolikelihood = -12833.216  
Iteration 3:   log pseudolikelihood = -12806.373  
Iteration 4:   log pseudolikelihood = -12806.273  
Iteration 5:   log pseudolikelihood = -12806.273  

Probit regression                                 Number of obs   =    1187663
                                                  Wald chi2(12)   =    2873.32
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -12806.273                 Pseudo R2       =     0.2648

                             (Std. Err. adjusted for 33255 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      genrep |   .2261656   .0509729     4.44   0.000     .1262606    .3260706
    midsin10 |   .0105789   .0010695     9.89   0.000     .0084827     .012675
    capratio |  -.5771746   .0844391    -6.84   0.000    -.7426722    -.411677
      capsum |   2.839928   .2170566    13.08   0.000     2.414505    3.265352
       demlo |  -.0061423   .0019372    -3.17   0.002    -.0099391   -.0023455
    atopally |   .0773432    .031878     2.43   0.015     .0148635    .1398229
    cntgdumy |   1.120099   .0352734    31.75   0.000     1.050964    1.189234
    bothmajr |  -.2043638    .107117    -1.91   0.056    -.4143092    .0055817
    onemajor |   .2644865   .0409149     6.46   0.000     .1842948    .3446781
           t |  -.0400487   .0027626   -14.50   0.000    -.0454633   -.0346341
          t2 |   .0006215   .0000655     9.48   0.000      .000493      .00075
          t3 |  -2.58e-06   3.88e-07    -6.65   0.000    -3.34e-06   -1.82e-06
       _cons |  -2.510593   .0745621   -33.67   0.000    -2.656732   -2.364454
------------------------------------------------------------------------------

. 
. ** Model 6
. 
. xtset dyadid year
       panel variable:  dyadid (unbalanced)
        time variable:  year, 1816 to 2001, but with gaps
                delta:  1 unit

. xtlogit mzmidl backdown1 midsin10 capratio capsum demlo atopally cntgdumy both
> majr onemajor t t2 t3, fe
note: multiple positive outcomes within groups encountered.
note: 32384 groups (1114905 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log likelihood = -8151.1177  
Iteration 1:   log likelihood = -8019.3443  
Iteration 2:   log likelihood = -8017.8836  
Iteration 3:   log likelihood = -8017.8834  

Conditional fixed-effects logistic regression   Number of obs      =     72758
Group variable: dyadid                          Number of groups   =       871

                                                Obs per group: min =         4
                                                               avg =      83.5
                                                               max =       185

                                                LR chi2(12)        =    449.50
Log likelihood  = -8017.8834                    Prob > chi2        =    0.0000

------------------------------------------------------------------------------
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   backdown1 |   .2944532   .0916685     3.21   0.001     .1147862    .4741201
    midsin10 |   .0232665   .0025102     9.27   0.000     .0183466    .0281863
    capratio |  -.4885351   .3378693    -1.45   0.148    -1.150747    .1736766
      capsum |   2.482354   .5506874     4.51   0.000     1.403026    3.561681
       demlo |  -.0202359   .0055679    -3.63   0.000    -.0311489    -.009323
    atopally |  -.0899245   .0632063    -1.42   0.155    -.2138066    .0339577
    cntgdumy |   .7614137   .1733873     4.39   0.000     .4215809    1.101247
    bothmajr |   .0603852   .1558149     0.39   0.698    -.2450064    .3657767
    onemajor |    .676894   .1157356     5.85   0.000     .4500563    .9037317
           t |  -.0537904    .004857   -11.07   0.000    -.0633099   -.0442709
          t2 |   .0009009   .0001075     8.38   0.000     .0006902    .0011116
          t3 |  -3.07e-06   6.12e-07    -5.02   0.000    -4.27e-06   -1.87e-06
------------------------------------------------------------------------------

. 
. 
. ** Table 2, Model 1
. 
. probit mzmidl goodrep midsin10 capratio capsum demlo atopally cntgdumy bothmaj
> r onemajor t t2 t3, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -17417.987  
Iteration 1:   log pseudolikelihood = -13537.114  
Iteration 2:   log pseudolikelihood = -12842.958  
Iteration 3:   log pseudolikelihood =  -12816.61  
Iteration 4:   log pseudolikelihood = -12816.512  
Iteration 5:   log pseudolikelihood = -12816.512  

Probit regression                                 Number of obs   =    1187663
                                                  Wald chi2(12)   =    2901.99
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -12816.512                 Pseudo R2       =     0.2642

                             (Std. Err. adjusted for 33255 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     goodrep |  -.0968329   .0480137    -2.02   0.044     -.190938   -.0027279
    midsin10 |    .010919   .0010664    10.24   0.000     .0088288    .0130092
    capratio |  -.5897017   .0847988    -6.95   0.000    -.7559042   -.4234991
      capsum |   2.868021   .2210774    12.97   0.000     2.434718    3.301325
       demlo |  -.0062422   .0019264    -3.24   0.001    -.0100178   -.0024666
    atopally |    .074683   .0318823     2.34   0.019     .0121948    .1371712
    cntgdumy |   1.122281   .0353826    31.72   0.000     1.052933     1.19163
    bothmajr |  -.2146676   .1085886    -1.98   0.048    -.4274973   -.0018379
    onemajor |   .2691359   .0414265     6.50   0.000     .1879415    .3503303
           t |  -.0402756   .0027705   -14.54   0.000    -.0457057   -.0348456
          t2 |   .0006256   .0000656     9.53   0.000      .000497    .0007543
          t3 |  -2.60e-06   3.88e-07    -6.69   0.000    -3.36e-06   -1.84e-06
       _cons |  -2.495877    .075055   -33.25   0.000    -2.642982   -2.348772
------------------------------------------------------------------------------

. 
. ** Model 2
. 
. probit mzmidl backdown1 midsin10 capratio capsum demlo atopally cntgdumy bothm
> ajr onemajor t t2 t3 if ccode1!=ccode_alt, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -16619.086  
Iteration 1:   log pseudolikelihood = -12999.348  
Iteration 2:   log pseudolikelihood = -12370.152  
Iteration 3:   log pseudolikelihood = -12349.072  
Iteration 4:   log pseudolikelihood = -12348.992  
Iteration 5:   log pseudolikelihood = -12348.992  

Probit regression                                 Number of obs   =    1186278
                                                  Wald chi2(12)   =    2723.53
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -12348.992                 Pseudo R2       =     0.2569

                             (Std. Err. adjusted for 33255 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   backdown1 |   .1331581   .0563053     2.36   0.018     .0228018    .2435144
    midsin10 |   .0099387   .0010848     9.16   0.000     .0078126    .0120648
    capratio |  -.5815855    .085399    -6.81   0.000    -.7489644   -.4142066
      capsum |   2.784626    .221508    12.57   0.000     2.350479    3.218774
       demlo |  -.0065232     .00197    -3.31   0.001    -.0103844   -.0026621
    atopally |   .0873895   .0320701     2.72   0.006     .0245332    .1502458
    cntgdumy |   1.113065   .0348311    31.96   0.000     1.044797    1.181332
    bothmajr |  -.2034688   .1086411    -1.87   0.061    -.4164014    .0094638
    onemajor |   .2645179   .0408732     6.47   0.000      .184408    .3446278
           t |  -.0389425   .0027103   -14.37   0.000    -.0442546   -.0336305
          t2 |   .0006043   .0000641     9.43   0.000     .0004787    .0007299
          t3 |  -2.50e-06   3.78e-07    -6.63   0.000    -3.24e-06   -1.76e-06
       _cons |   -2.52828    .074409   -33.98   0.000    -2.674119   -2.382441
------------------------------------------------------------------------------

. 
. ** Model 3
. 
. probit terrmidl backdown_terr1 backdown_nonterr1 midsin10 capratio capsum deml
> o atopally cntgdumy bothmajr onemajor tterr tterr2 tterr3, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -5696.5149  
Iteration 1:   log pseudolikelihood = -4327.4631  
Iteration 2:   log pseudolikelihood = -4006.9164  
Iteration 3:   log pseudolikelihood =  -4000.644  
Iteration 4:   log pseudolikelihood = -4000.6003  
Iteration 5:   log pseudolikelihood = -4000.6003  

Probit regression                                 Number of obs   =    1187663
                                                  Wald chi2(13)   =    1655.12
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4000.6003                 Pseudo R2       =     0.2977

                             (Std. Err. adjusted for 33255 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
    terrmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
backdown_t~1 |   .7499664   .1671777     4.49   0.000     .4223042    1.077629
backdown_n~1 |   .1679761    .091562     1.83   0.067    -.0114821    .3474342
    midsin10 |   .0096437   .0019577     4.93   0.000     .0058066    .0134808
    capratio |  -.7351148   .1409283    -5.22   0.000    -1.011329   -.4589004
      capsum |   1.418414   .4112213     3.45   0.001     .6124356    2.224393
       demlo |  -.0029083   .0032112    -0.91   0.365    -.0092023    .0033856
    atopally |    .021376   .0494827     0.43   0.666    -.0756083    .1183603
    cntgdumy |   1.326998   .0591835    22.42   0.000        1.211    1.442995
    bothmajr |  -.1461829   .1233342    -1.19   0.236    -.3879134    .0955476
    onemajor |   .1795108   .0723114     2.48   0.013      .037783    .3212386
       tterr |   -.044289   .0045965    -9.64   0.000    -.0532979     -.03528
      tterr2 |   .0006573   .0000957     6.87   0.000     .0004697    .0008449
      tterr3 |  -2.64e-06   5.18e-07    -5.10   0.000    -3.66e-06   -1.63e-06
       _cons |  -2.765561   .1229185   -22.50   0.000    -3.006477   -2.524645
------------------------------------------------------------------------------

. 
. ** Model 4
. 
. use repdata_leaders

. probit mzleadmidl backdown1_leader backdown1_newleader midsin10 capratio capsu
> m demlo atopally cntgdumy bothmajr onemajor t t2 t3 [iweight=yrpct], cluster(d
> yadid)

Iteration 0:   log pseudolikelihood = -13571.131  
Iteration 1:   log pseudolikelihood = -10355.505  
Iteration 2:   log pseudolikelihood = -9765.3073  
Iteration 3:   log pseudolikelihood = -9739.1961  
Iteration 4:   log pseudolikelihood = -9739.1158  
Iteration 5:   log pseudolikelihood = -9739.1158  

Probit regression                                 Number of obs   =    1021833
                                                  Wald chi2(13)   =    2680.87
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -9739.1158                 Pseudo R2       =     0.2824

                             (Std. Err. adjusted for 26834 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
  mzleadmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
bac~1_leader |   .2393566   .0629512     3.80   0.000     .1159745    .3627388
backdown1_n~ |   .2063437   .0850249     2.43   0.015     .0396979    .3729895
    midsin10 |   .0070417   .0013334     5.28   0.000     .0044284    .0096551
    capratio |  -.5100355   .0966376    -5.28   0.000    -.6994416   -.3206294
      capsum |   3.087793   .2456803    12.57   0.000     2.606268    3.569317
       demlo |  -.0082345   .0023384    -3.52   0.000    -.0128177   -.0036514
    atopally |   .0911244   .0341031     2.67   0.008     .0242835    .1579652
    cntgdumy |   1.174369   .0359996    32.62   0.000     1.103811    1.244927
    bothmajr |  -.2062743    .107962    -1.91   0.056    -.4178759    .0053273
    onemajor |   .2827155   .0433332     6.52   0.000     .1977839    .3676471
           t |   -.040981   .0030537   -13.42   0.000    -.0469661   -.0349959
          t2 |   .0006135   .0000718     8.54   0.000     .0004728    .0007543
          t3 |  -2.48e-06   4.20e-07    -5.90   0.000    -3.30e-06   -1.66e-06
       _cons |  -2.573975   .0821458   -31.33   0.000    -2.734978   -2.412972
------------------------------------------------------------------------------

. 
. ******************************************************************************
> *********************************************
. ** In the main text, we note that findings are robust to a variety of changes 
> to variable coding and model specification.
. ** This section reproduces the regressions necessary to substantiate those cla
> ims.  Specifically, we note that the findings
. ** are robust to the following changes:
. ** 1) coding backing down in different ways
. ** 2) limiting the analysis to politically relevant dyads
. ** 3) controlling for or omitting important historical time periods
. ** 4) using fatal MIDs instead of all MIDs
. 
. ** We demonstrate robustness from the baseline model (Table 1, Model 2).
. ** First, for comparison, the baseline model
. probit mzmidl backdown1 midsin10 capratio capsum demlo atopally cntgdumy bothm
> ajr onemajor t t2 t3, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -17417.987  
Iteration 1:   log pseudolikelihood = -13526.396  
Iteration 2:   log pseudolikelihood = -12831.406  
Iteration 3:   log pseudolikelihood =  -12804.65  
Iteration 4:   log pseudolikelihood = -12804.549  
Iteration 5:   log pseudolikelihood = -12804.549  

Probit regression                                 Number of obs   =    1187663
                                                  Wald chi2(12)   =    2822.73
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -12804.549                 Pseudo R2       =     0.2649

                             (Std. Err. adjusted for 33255 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   backdown1 |   .1965937   .0556578     3.53   0.000     .0875063    .3056811
    midsin10 |   .0098363   .0011075     8.88   0.000     .0076657    .0120069
    capratio |  -.5741762   .0844788    -6.80   0.000    -.7397516   -.4086008
      capsum |   2.815476   .2174824    12.95   0.000     2.389218    3.241733
       demlo |  -.0066511   .0019487    -3.41   0.001    -.0104705   -.0028316
    atopally |    .080123    .031876     2.51   0.012     .0176472    .1425988
    cntgdumy |    1.11962   .0353688    31.66   0.000     1.050298    1.188941
    bothmajr |  -.2233379   .1078038    -2.07   0.038    -.4346295   -.0120462
    onemajor |   .2555515   .0408026     6.26   0.000     .1755798    .3355232
           t |  -.0400719   .0027613   -14.51   0.000     -.045484   -.0346597
          t2 |   .0006219   .0000654     9.51   0.000     .0004937    .0007502
          t3 |  -2.58e-06   3.87e-07    -6.67   0.000    -3.34e-06   -1.82e-06
       _cons |  -2.518771     .07445   -33.83   0.000     -2.66469   -2.372851
------------------------------------------------------------------------------

. 
. ** Next, robustness to changing the way in which a state is coded as acquiring
>  a bad reputation.  We use a total of seven
. ** different measures that differ in various ways from the main one (which cod
> es a country as backing down if it yielded
. ** in a MID without fighting, and codes it as acquiring a bad reputation that 
> declines in significance over the subsequent
. ** ten years).
. 
. ** Bad reputation from any yielding (including yielding after using force)
. probit mzmidl backdown2 midsin10 capratio capsum demlo atopally cntgdumy bothm
> ajr onemajor t t2 t3, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -17417.987  
Iteration 1:   log pseudolikelihood = -13528.945  
Iteration 2:   log pseudolikelihood = -12833.407  
Iteration 3:   log pseudolikelihood = -12806.602  
Iteration 4:   log pseudolikelihood = -12806.501  
Iteration 5:   log pseudolikelihood = -12806.501  

Probit regression                                 Number of obs   =    1187663
                                                  Wald chi2(12)   =    2813.91
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -12806.501                 Pseudo R2       =     0.2648

                             (Std. Err. adjusted for 33255 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   backdown2 |   .1621839   .0487304     3.33   0.001     .0666741    .2576936
    midsin10 |   .0096199   .0011242     8.56   0.000     .0074166    .0118232
    capratio |  -.5733641   .0846642    -6.77   0.000    -.7393028   -.4074254
      capsum |   2.822492   .2175187    12.98   0.000     2.396163    3.248821
       demlo |   -.006389   .0019419    -3.29   0.001    -.0101951   -.0025829
    atopally |   .0809784   .0318725     2.54   0.011     .0185094    .1434473
    cntgdumy |   1.119114   .0354326    31.58   0.000     1.049668    1.188561
    bothmajr |  -.2244852   .1079867    -2.08   0.038    -.4361353   -.0128352
    onemajor |   .2542808   .0408537     6.22   0.000     .1742091    .3343526
           t |  -.0400256   .0027575   -14.52   0.000    -.0454302    -.034621
          t2 |   .0006202   .0000653     9.50   0.000     .0004923    .0007482
          t3 |  -2.57e-06   3.86e-07    -6.66   0.000    -3.33e-06   -1.81e-06
       _cons |   -2.51888   .0746263   -33.75   0.000    -2.665145   -2.372615
------------------------------------------------------------------------------

. 
. ** Bad reputation from yielding without escalating to war (hiact<20)
. probit mzmidl backdown3 midsin10 capratio capsum demlo atopally cntgdumy bothm
> ajr onemajor t t2 t3, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -17417.987  
Iteration 1:   log pseudolikelihood = -13529.914  
Iteration 2:   log pseudolikelihood = -12835.199  
Iteration 3:   log pseudolikelihood = -12808.484  
Iteration 4:   log pseudolikelihood = -12808.383  
Iteration 5:   log pseudolikelihood = -12808.383  

Probit regression                                 Number of obs   =    1187663
                                                  Wald chi2(12)   =    2819.08
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -12808.383                 Pseudo R2       =     0.2646

                             (Std. Err. adjusted for 33255 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   backdown3 |    .151636   .0492459     3.08   0.002     .0551158    .2481561
    midsin10 |   .0097361   .0011313     8.61   0.000     .0075189    .0119534
    capratio |  -.5748573   .0846879    -6.79   0.000    -.7408426    -.408872
      capsum |   2.822637   .2174743    12.98   0.000     2.396396    3.248879
       demlo |  -.0064237   .0019428    -3.31   0.001    -.0102315    -.002616
    atopally |   .0805607   .0318855     2.53   0.012     .0180663    .1430551
    cntgdumy |   1.119282   .0354408    31.58   0.000      1.04982    1.188745
    bothmajr |  -.2243379   .1079915    -2.08   0.038    -.4359974   -.0126784
    onemajor |   .2545052   .0408681     6.23   0.000     .1744052    .3346053
           t |  -.0400353   .0027591   -14.51   0.000     -.045443   -.0346275
          t2 |   .0006206   .0000653     9.50   0.000     .0004925    .0007487
          t3 |  -2.57e-06   3.87e-07    -6.66   0.000    -3.33e-06   -1.82e-06
       _cons |  -2.517392   .0746333   -33.73   0.000    -2.663671   -2.371114
------------------------------------------------------------------------------

. 
. ** Bad reputation from yielding or losing in a MID
. probit mzmidl backdown4 midsin10 capratio capsum demlo atopally cntgdumy bothm
> ajr onemajor t t2 t3, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -17417.987  
Iteration 1:   log pseudolikelihood = -13511.161  
Iteration 2:   log pseudolikelihood = -12800.984  
Iteration 3:   log pseudolikelihood = -12772.682  
Iteration 4:   log pseudolikelihood =  -12772.58  
Iteration 5:   log pseudolikelihood =  -12772.58  

Probit regression                                 Number of obs   =    1187663
                                                  Wald chi2(12)   =    2835.37
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood =  -12772.58                 Pseudo R2       =     0.2667

                             (Std. Err. adjusted for 33255 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   backdown4 |    .238642   .0342971     6.96   0.000      .171421     .305863
    midsin10 |   .0084835   .0011723     7.24   0.000     .0061858    .0107812
    capratio |  -.5594029   .0849082    -6.59   0.000    -.7258199    -.392986
      capsum |   2.810283   .2147635    13.09   0.000     2.389354    3.231212
       demlo |  -.0064144    .001954    -3.28   0.001     -.010244   -.0025847
    atopally |    .086731    .032075     2.70   0.007     .0238652    .1495969
    cntgdumy |   1.117959   .0353946    31.59   0.000     1.048587    1.187331
    bothmajr |  -.2248055   .1075219    -2.09   0.037    -.4355445   -.0140665
    onemajor |   .2494476   .0406913     6.13   0.000     .1696941     .329201
           t |  -.0397966   .0027476   -14.48   0.000    -.0451819   -.0344114
          t2 |   .0006158   .0000649     9.48   0.000     .0004886    .0007431
          t3 |  -2.55e-06   3.84e-07    -6.64   0.000    -3.30e-06   -1.80e-06
       _cons |  -2.549362   .0746936   -34.13   0.000    -2.695758   -2.402965
------------------------------------------------------------------------------

. 
. ** Bad reputation from yielding or losing in a MID without escalating to war
. probit mzmidl backdown5 midsin10 capratio capsum demlo atopally cntgdumy bothm
> ajr onemajor t t2 t3, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -17417.987  
Iteration 1:   log pseudolikelihood =  -13519.78  
Iteration 2:   log pseudolikelihood =   -12819.6  
Iteration 3:   log pseudolikelihood =  -12792.24  
Iteration 4:   log pseudolikelihood = -12792.138  
Iteration 5:   log pseudolikelihood = -12792.138  

Probit regression                                 Number of obs   =    1187663
                                                  Wald chi2(12)   =    2829.45
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -12792.138                 Pseudo R2       =     0.2656

                             (Std. Err. adjusted for 33255 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   backdown5 |   .1959246   .0381376     5.14   0.000     .1211764    .2706729
    midsin10 |   .0091316   .0011723     7.79   0.000     .0068339    .0114293
    capratio |   -.565261   .0846997    -6.67   0.000    -.7312694   -.3992527
      capsum |     2.8075   .2155221    13.03   0.000     2.385085    3.229916
       demlo |   -.006586   .0019521    -3.37   0.001    -.0104121     -.00276
    atopally |   .0843684    .032093     2.63   0.009     .0214672    .1472696
    cntgdumy |   1.118583   .0354304    31.57   0.000     1.049141    1.188025
    bothmajr |  -.2232318   .1077336    -2.07   0.038    -.4343858   -.0120777
    onemajor |   .2499985   .0408621     6.12   0.000     .1699102    .3300868
           t |  -.0398437   .0027536   -14.47   0.000    -.0452406   -.0344468
          t2 |    .000617   .0000652     9.47   0.000     .0004893    .0007447
          t3 |  -2.56e-06   3.86e-07    -6.63   0.000    -3.31e-06   -1.80e-06
       _cons |  -2.535937   .0743833   -34.09   0.000    -2.681725   -2.390148
------------------------------------------------------------------------------

. 
. ** Same as baseline variable, but reputation phases out over 5 years instead o
> f 10
. probit mzmidl backdown6 midsin10 capratio capsum demlo atopally cntgdumy bothm
> ajr onemajor t t2 t3, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -17417.987  
Iteration 1:   log pseudolikelihood =  -13523.43  
Iteration 2:   log pseudolikelihood = -12826.773  
Iteration 3:   log pseudolikelihood = -12799.768  
Iteration 4:   log pseudolikelihood = -12799.667  
Iteration 5:   log pseudolikelihood = -12799.667  

Probit regression                                 Number of obs   =    1187663
                                                  Wald chi2(12)   =    2835.37
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -12799.667                 Pseudo R2       =     0.2651

                             (Std. Err. adjusted for 33255 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   backdown6 |   .2590491   .0549078     4.72   0.000     .1514317    .3666665
    midsin10 |    .010126   .0010912     9.28   0.000     .0079874    .0122647
    capratio |  -.5763293   .0845335    -6.82   0.000     -.742012   -.4106467
      capsum |   2.824439   .2178471    12.97   0.000     2.397467    3.251412
       demlo |   -.006649    .001943    -3.42   0.001    -.0104573   -.0028407
    atopally |   .0782665   .0318443     2.46   0.014     .0158528    .1406802
    cntgdumy |   1.120505    .035313    31.73   0.000     1.051293    1.189717
    bothmajr |  -.2233105    .107868    -2.07   0.038    -.4347279   -.0118931
    onemajor |    .253965   .0408969     6.21   0.000     .1738085    .3341214
           t |  -.0400436   .0027604   -14.51   0.000    -.0454539   -.0346334
          t2 |   .0006213   .0000654     9.50   0.000     .0004931    .0007494
          t3 |  -2.58e-06   3.87e-07    -6.67   0.000    -3.34e-06   -1.82e-06
       _cons |  -2.517596    .074525   -33.78   0.000    -2.663662    -2.37153
------------------------------------------------------------------------------

. 
. ** Same as baseline variable, but reputation phases out over 20 years instead 
> of 10
. probit mzmidl backdown7 midsin10 capratio capsum demlo atopally cntgdumy bothm
> ajr onemajor t t2 t3, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -17417.987  
Iteration 1:   log pseudolikelihood = -13529.232  
Iteration 2:   log pseudolikelihood = -12834.858  
Iteration 3:   log pseudolikelihood = -12808.299  
Iteration 4:   log pseudolikelihood = -12808.199  
Iteration 5:   log pseudolikelihood = -12808.199  

Probit regression                                 Number of obs   =    1187663
                                                  Wald chi2(12)   =    2815.53
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -12808.199                 Pseudo R2       =     0.2647

                             (Std. Err. adjusted for 33255 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   backdown7 |   .1491829   .0547354     2.73   0.006     .0419035    .2564624
    midsin10 |   .0095601   .0011526     8.29   0.000      .007301    .0118191
    capratio |  -.5732329   .0845319    -6.78   0.000    -.7389124   -.4075534
      capsum |   2.812533   .2174133    12.94   0.000     2.386411    3.238655
       demlo |  -.0067142   .0019563    -3.43   0.001    -.0105484     -.00288
    atopally |   .0813975   .0319062     2.55   0.011     .0188625    .1439326
    cntgdumy |   1.119219   .0354236    31.60   0.000      1.04979    1.188648
    bothmajr |  -.2248641   .1078837    -2.08   0.037    -.4363123   -.0134159
    onemajor |   .2546791   .0407833     6.24   0.000     .1747453    .3346129
           t |  -.0401619   .0027628   -14.54   0.000    -.0455769   -.0347468
          t2 |   .0006235   .0000654     9.53   0.000     .0004953    .0007517
          t3 |  -2.59e-06   3.87e-07    -6.69   0.000    -3.35e-06   -1.83e-06
       _cons |  -2.519187   .0744377   -33.84   0.000    -2.665082   -2.373291
------------------------------------------------------------------------------

. 
. ** Alternate variable analogous to AltRep_it that focuses only on how frequent
> ly in recent MIDs the country has backed
. ** down (i.e. no good reputation)
. probit mzmidl badallm midsin10 capratio capsum demlo atopally cntgdumy bothmaj
> r onemajor t t2 t3, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -17417.987  
Iteration 1:   log pseudolikelihood = -13534.752  
Iteration 2:   log pseudolikelihood = -12838.882  
Iteration 3:   log pseudolikelihood =  -12812.23  
Iteration 4:   log pseudolikelihood =  -12812.13  
Iteration 5:   log pseudolikelihood =  -12812.13  

Probit regression                                 Number of obs   =    1187663
                                                  Wald chi2(12)   =    2859.35
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood =  -12812.13                 Pseudo R2       =     0.2644

                             (Std. Err. adjusted for 33255 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     badallm |   .2177329   .0668148     3.26   0.001     .0867783    .3486876
    midsin10 |   .0106109   .0010732     9.89   0.000     .0085075    .0127143
    capratio |  -.5774615   .0844782    -6.84   0.000    -.7430357   -.4118874
      capsum |   2.823423   .2167718    13.02   0.000     2.398558    3.248288
       demlo |  -.0064219   .0019403    -3.31   0.001    -.0102249   -.0026189
    atopally |    .078366   .0318155     2.46   0.014     .0160088    .1407231
    cntgdumy |   1.119786   .0353408    31.69   0.000     1.050519    1.189052
    bothmajr |  -.2161974    .107598    -2.01   0.045    -.4270856   -.0053092
    onemajor |   .2590993   .0407885     6.35   0.000     .1791553    .3390434
           t |  -.0401663   .0027628   -14.54   0.000    -.0455813   -.0347512
          t2 |   .0006234   .0000654     9.53   0.000     .0004952    .0007517
          t3 |  -2.59e-06   3.87e-07    -6.68   0.000    -3.35e-06   -1.83e-06
       _cons |  -2.515977   .0744139   -33.81   0.000    -2.661825   -2.370128
------------------------------------------------------------------------------

. 
. ** For comparison purposes, the same approach looking only at how frequently i
> n recent MIDs the country has fought and
. ** won (i.e. no bad reputation) -- this analysis compares to Table 2, Model 1.
. probit mzmidl goodallm midsin10 capratio capsum demlo atopally cntgdumy bothma
> jr onemajor t t2 t3, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -17417.987  
Iteration 1:   log pseudolikelihood = -13537.497  
Iteration 2:   log pseudolikelihood = -12840.988  
Iteration 3:   log pseudolikelihood = -12814.344  
Iteration 4:   log pseudolikelihood = -12814.247  
Iteration 5:   log pseudolikelihood = -12814.247  

Probit regression                                 Number of obs   =    1187663
                                                  Wald chi2(12)   =    2906.57
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -12814.247                 Pseudo R2       =     0.2643

                             (Std. Err. adjusted for 33255 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    goodallm |   -.251786   .0914276    -2.75   0.006    -.4309807   -.0725912
    midsin10 |   .0103794   .0010743     9.66   0.000     .0082739    .0124849
    capratio |  -.5872687   .0848227    -6.92   0.000     -.753518   -.4210193
      capsum |   2.879783   .2185922    13.17   0.000      2.45135    3.308216
       demlo |  -.0061893   .0019313    -3.20   0.001    -.0099746   -.0024039
    atopally |   .0742179    .031901     2.33   0.020      .011693    .1367427
    cntgdumy |   1.122296   .0353774    31.72   0.000     1.052958    1.191635
    bothmajr |  -.2109035   .1082734    -1.95   0.051    -.4231155    .0013085
    onemajor |   .2677476   .0411126     6.51   0.000     .1871684    .3483268
           t |  -.0401079     .00276   -14.53   0.000    -.0455174   -.0346985
          t2 |   .0006222   .0000655     9.50   0.000     .0004939    .0007506
          t3 |  -2.58e-06   3.88e-07    -6.66   0.000    -3.34e-06   -1.82e-06
       _cons |  -2.494357   .0751819   -33.18   0.000     -2.64171   -2.347003
------------------------------------------------------------------------------

. 
. ** We also use two alternate codings of reputation from the ICB dataset
. 
. ** First, coding any country that loses a crisis as getting a bad reputation (
> even if it used force)
. probit mzmidl bkdown2 midsin10 capratio capsum demlo atopally cntgdumy bothmaj
> r onemajor t t2 t3, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -17417.987  
Iteration 1:   log pseudolikelihood = -13531.533  
Iteration 2:   log pseudolikelihood = -12837.117  
Iteration 3:   log pseudolikelihood = -12810.697  
Iteration 4:   log pseudolikelihood = -12810.599  
Iteration 5:   log pseudolikelihood = -12810.599  

Probit regression                                 Number of obs   =    1187663
                                                  Wald chi2(12)   =    2878.88
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -12810.599                 Pseudo R2       =     0.2645

                             (Std. Err. adjusted for 33255 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     bkdown2 |   .1543081   .0454749     3.39   0.001      .065179    .2434372
    midsin10 |   .0091869    .001161     7.91   0.000     .0069115    .0114624
    capratio |  -.5840512   .0856088    -6.82   0.000    -.7518414   -.4162611
      capsum |   2.850275   .2182472    13.06   0.000     2.422519    3.278032
       demlo |  -.0063101   .0019374    -3.26   0.001    -.0101073   -.0025129
    atopally |   .0717455   .0317477     2.26   0.024     .0095213    .1339698
    cntgdumy |    1.12355   .0353913    31.75   0.000     1.054184    1.192915
    bothmajr |  -.2180245   .1085979    -2.01   0.045    -.4308726   -.0051765
    onemajor |   .2657254   .0410262     6.48   0.000     .1853155    .3461353
           t |  -.0400153    .002772   -14.44   0.000    -.0454484   -.0345823
          t2 |   .0006211   .0000654     9.49   0.000     .0004929    .0007494
          t3 |  -2.58e-06   3.87e-07    -6.66   0.000    -3.34e-06   -1.82e-06
       _cons |  -2.509378   .0760721   -32.99   0.000    -2.658477    -2.36028
------------------------------------------------------------------------------

. 
. ** Second, coding a country that loses a crisis that did not escalate to war a
> s getting a bad reputation
. probit mzmidl bkdown3 midsin10 capratio capsum demlo atopally cntgdumy bothmaj
> r onemajor t t2 t3, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -17417.987  
Iteration 1:   log pseudolikelihood = -13531.945  
Iteration 2:   log pseudolikelihood =  -12838.17  
Iteration 3:   log pseudolikelihood = -12811.845  
Iteration 4:   log pseudolikelihood = -12811.747  
Iteration 5:   log pseudolikelihood = -12811.747  

Probit regression                                 Number of obs   =    1187663
                                                  Wald chi2(12)   =    2877.31
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -12811.747                 Pseudo R2       =     0.2645

                             (Std. Err. adjusted for 33255 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     bkdown3 |   .1525249   .0476951     3.20   0.001     .0590443    .2460055
    midsin10 |   .0092426   .0011703     7.90   0.000     .0069489    .0115364
    capratio |  -.5853572   .0856524    -6.83   0.000    -.7532328   -.4174816
      capsum |    2.84807   .2186949    13.02   0.000     2.419436    3.276704
       demlo |  -.0063255   .0019371    -3.27   0.001    -.0101222   -.0025288
    atopally |    .071969   .0317046     2.27   0.023     .0098291    .1341089
    cntgdumy |   1.123625   .0353814    31.76   0.000     1.054279    1.192972
    bothmajr |  -.2186043   .1086536    -2.01   0.044    -.4315615   -.0056471
    onemajor |   .2663077   .0410661     6.48   0.000     .1858197    .3467958
           t |  -.0400816   .0027706   -14.47   0.000    -.0455118   -.0346514
          t2 |   .0006222   .0000655     9.51   0.000     .0004939    .0007505
          t3 |  -2.58e-06   3.87e-07    -6.67   0.000    -3.34e-06   -1.82e-06
       _cons |    -2.5068   .0760327   -32.97   0.000    -2.655822   -2.357779
------------------------------------------------------------------------------

. 
. ** Limiting the analysis to politically relevant dyads
. 
. probit mzmidl backdown1 midsin10 capratio capsum demlo atopally cntgdumy bothm
> ajr onemajor t t2 t3 if pol_rel==1, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -11021.714  
Iteration 1:   log pseudolikelihood = -9632.7081  
Iteration 2:   log pseudolikelihood =  -9459.191  
Iteration 3:   log pseudolikelihood = -9458.2662  
Iteration 4:   log pseudolikelihood = -9458.2659  

Probit regression                                 Number of obs   =     162389
                                                  Wald chi2(12)   =     916.88
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -9458.2659                 Pseudo R2       =     0.1419

                              (Std. Err. adjusted for 3424 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   backdown1 |   .1577415   .0529347     2.98   0.003     .0539914    .2614915
    midsin10 |    .006701   .0010578     6.33   0.000     .0046277    .0087743
    capratio |  -.8690269   .1110038    -7.83   0.000     -1.08659   -.6514635
      capsum |   1.496612   .1773594     8.44   0.000     1.148994     1.84423
       demlo |  -.0093579   .0021581    -4.34   0.000    -.0135876   -.0051282
    atopally |  -.0089586   .0317476    -0.28   0.778    -.0711828    .0532656
    cntgdumy |   .6041649   .0441141    13.70   0.000     .5177029    .6906269
    bothmajr |  -.0000254   .0782353    -0.00   1.000    -.1533638     .153313
    onemajor |  -.0605377   .0372881    -1.62   0.104     -.133621    .0125457
           t |  -.0443252   .0030282   -14.64   0.000    -.0502604     -.03839
          t2 |   .0006574   .0000689     9.54   0.000     .0005223    .0007924
          t3 |  -2.64e-06   3.94e-07    -6.69   0.000    -3.41e-06   -1.87e-06
       _cons |  -1.472244   .1078885   -13.65   0.000    -1.683702   -1.260786
------------------------------------------------------------------------------

. 
. 
. ** Controls for important time periods
. 
. gen prewwi=0

. replace prewwi=1 if year<1914
(144294 real changes made)

. gen interwar=0

. replace interwar=1 if year>=1914 & year<=1945
(149623 real changes made)

. gen coldwar=0

. replace coldwar=1 if year>1945 & year<1990
(822890 real changes made)

. gen postcw=0

. replace postcw=1 if year>1989
(478029 real changes made)

. 
. probit mzmidl backdown1 midsin10 capratio capsum demlo atopally cntgdumy bothm
> ajr onemajor prewwi interwar coldwar t t2 t3, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -17417.987  
Iteration 1:   log pseudolikelihood = -13500.353  
Iteration 2:   log pseudolikelihood = -12800.978  
Iteration 3:   log pseudolikelihood = -12772.855  
Iteration 4:   log pseudolikelihood = -12772.749  
Iteration 5:   log pseudolikelihood = -12772.749  

Probit regression                                 Number of obs   =    1187663
                                                  Wald chi2(15)   =    3110.61
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -12772.749                 Pseudo R2       =     0.2667

                             (Std. Err. adjusted for 33255 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   backdown1 |   .1883766   .0538581     3.50   0.000     .0828166    .2939365
    midsin10 |   .0092712   .0011258     8.24   0.000     .0070647    .0114776
    capratio |  -.5734091   .0852515    -6.73   0.000    -.7404989   -.4063193
      capsum |   2.930472   .2134446    13.73   0.000     2.512129    3.348816
       demlo |  -.0091373    .002146    -4.26   0.000    -.0133434   -.0049312
    atopally |   .0746796   .0332266     2.25   0.025     .0095568    .1398024
    cntgdumy |   1.127489   .0363425    31.02   0.000     1.056259    1.198719
    bothmajr |  -.2205449   .1066139    -2.07   0.039    -.4295043   -.0115855
    onemajor |   .2503954   .0414053     6.05   0.000     .1692426    .3315483
      prewwi |  -.1271037   .0522101    -2.43   0.015    -.2294336   -.0247738
    interwar |   .0590952   .0353192     1.67   0.094    -.0101292    .1283196
     coldwar |  -.0932309   .0293704    -3.17   0.002    -.1507957    -.035666
           t |  -.0392735   .0027702   -14.18   0.000     -.044703    -.033844
          t2 |   .0006032   .0000662     9.11   0.000     .0004735     .000733
          t3 |  -2.49e-06   3.93e-07    -6.34   0.000    -3.26e-06   -1.72e-06
       _cons |  -2.477754   .0767091   -32.30   0.000    -2.628101   -2.327407
------------------------------------------------------------------------------

. probit mzmidl backdown1 midsin10 capratio capsum demlo atopally cntgdumy bothm
> ajr onemajor t t2 t3 if prewwi!=1, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -14254.874  
Iteration 1:   log pseudolikelihood = -10657.574  
Iteration 2:   log pseudolikelihood = -10033.173  
Iteration 3:   log pseudolikelihood = -10005.166  
Iteration 4:   log pseudolikelihood = -10005.102  
Iteration 5:   log pseudolikelihood = -10005.102  

Probit regression                                 Number of obs   =    1070305
                                                  Wald chi2(12)   =    2792.42
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -10005.102                 Pseudo R2       =     0.2981

                             (Std. Err. adjusted for 32367 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   backdown1 |   .1503101   .0622275     2.42   0.016     .0283465    .2722737
    midsin10 |    .007411   .0013094     5.66   0.000     .0048446    .0099774
    capratio |  -.6084359   .0935997    -6.50   0.000    -.7918881   -.4249838
      capsum |   2.872476   .2715966    10.58   0.000     2.340156    3.404796
       demlo |  -.0134542   .0022695    -5.93   0.000    -.0179022   -.0090061
    atopally |   .0647389   .0367459     1.76   0.078    -.0072818    .1367596
    cntgdumy |   1.243546   .0363445    34.22   0.000     1.172312     1.31478
    bothmajr |   .0456429   .1284137     0.36   0.722    -.2060433    .2973291
    onemajor |   .3521061   .0467437     7.53   0.000     .2604901    .4437222
           t |  -.0411068   .0033151   -12.40   0.000    -.0476043   -.0346094
          t2 |   .0006518   .0000801     8.14   0.000     .0004949    .0008088
          t3 |  -2.74e-06   4.74e-07    -5.78   0.000    -3.67e-06   -1.81e-06
       _cons |  -2.564353   .0817379   -31.37   0.000    -2.724556   -2.404149
------------------------------------------------------------------------------

. probit mzmidl backdown1 midsin10 capratio capsum demlo atopally cntgdumy bothm
> ajr onemajor t t2 t3 if interwar!=1, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -14455.486  
Iteration 1:   log pseudolikelihood = -11076.564  
Iteration 2:   log pseudolikelihood = -10469.232  
Iteration 3:   log pseudolikelihood =  -10445.24  
Iteration 4:   log pseudolikelihood = -10445.171  
Iteration 5:   log pseudolikelihood = -10445.171  

Probit regression                                 Number of obs   =    1080052
                                                  Wald chi2(12)   =    2495.63
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -10445.171                 Pseudo R2       =     0.2774

                             (Std. Err. adjusted for 33243 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   backdown1 |   .1776106   .0606976     2.93   0.003     .0586455    .2965758
    midsin10 |   .0107513   .0012392     8.68   0.000     .0083225    .0131802
    capratio |  -.6238131   .0904721    -6.90   0.000    -.8011351   -.4464911
      capsum |   3.248366   .2687735    12.09   0.000      2.72158    3.775152
       demlo |  -.0040083   .0020568    -1.95   0.051    -.0080396     .000023
    atopally |   .0700848   .0346098     2.02   0.043     .0022508    .1379188
    cntgdumy |   1.193952   .0381368    31.31   0.000     1.119205    1.268699
    bothmajr |  -.3067418   .1295472    -2.37   0.018    -.5606497    -.052834
    onemajor |    .092654   .0501428     1.85   0.065    -.0056241     .190932
           t |  -.0406219   .0030404   -13.36   0.000     -.046581   -.0346627
          t2 |   .0006092   .0000738     8.26   0.000     .0004646    .0007537
          t3 |  -2.47e-06   4.35e-07    -5.67   0.000    -3.32e-06   -1.62e-06
       _cons |  -2.481882   .0786207   -31.57   0.000    -2.635976   -2.327788
------------------------------------------------------------------------------

. probit mzmidl backdown1 midsin10 capratio capsum demlo atopally cntgdumy bothm
> ajr onemajor t t2 t3 if coldwar!=1, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -9317.8069  
Iteration 1:   log pseudolikelihood = -9143.0167  
Iteration 2:   log pseudolikelihood = -7324.5449  
Iteration 3:   log pseudolikelihood = -7314.4128  
Iteration 4:   log pseudolikelihood = -7314.3954  
Iteration 5:   log pseudolikelihood = -7314.3954  

Probit regression                                 Number of obs   =     537955
                                                  Wald chi2(12)   =    1881.07
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -7314.3954                 Pseudo R2       =     0.2150

                             (Std. Err. adjusted for 31642 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   backdown1 |   .2067162   .0577781     3.58   0.000     .0934731    .3199592
    midsin10 |    .010607   .0011893     8.92   0.000     .0082761     .012938
    capratio |  -.5597394   .1035023    -5.41   0.000    -.7626001   -.3568787
      capsum |   2.442554   .2433745    10.04   0.000     1.965549    2.919559
       demlo |  -.0072616   .0021571    -3.37   0.001    -.0114895   -.0030337
    atopally |   .1107882   .0381254     2.91   0.004     .0360638    .1855125
    cntgdumy |   .9066168   .0422313    21.47   0.000     .8238449    .9893886
    bothmajr |  -.2290731   .0952181    -2.41   0.016    -.4156972   -.0424491
    onemajor |   .3377157    .046407     7.28   0.000     .2467596    .4286717
           t |  -.0291867   .0028015   -10.42   0.000    -.0346775   -.0236959
          t2 |   .0004285   .0000616     6.95   0.000     .0003077    .0005492
          t3 |  -1.68e-06   3.53e-07    -4.76   0.000    -2.37e-06   -9.87e-07
       _cons |  -2.531676    .090458   -27.99   0.000     -2.70897   -2.354381
------------------------------------------------------------------------------

. probit mzmidl backdown1 midsin10 capratio capsum demlo atopally cntgdumy bothm
> ajr onemajor t t2 t3 if postcw!=1, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -14165.205  
Iteration 1:   log pseudolikelihood = -13930.784  
Iteration 2:   log pseudolikelihood = -10495.108  
Iteration 3:   log pseudolikelihood =  -10459.75  
Iteration 4:   log pseudolikelihood = -10459.575  
Iteration 5:   log pseudolikelihood = -10459.575  

Probit regression                                 Number of obs   =     874677
                                                  Wald chi2(12)   =    2332.92
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -10459.575                 Pseudo R2       =     0.2616

                             (Std. Err. adjusted for 24446 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
      mzmidl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   backdown1 |   .2456531    .060117     4.09   0.000     .1278259    .3634802
    midsin10 |   .0094819   .0012601     7.52   0.000     .0070121    .0119516
    capratio |  -.5051545   .0950376    -5.32   0.000    -.6914249   -.3188842
      capsum |   2.828952   .2163715    13.07   0.000     2.404872    3.253032
       demlo |   -.003625   .0022618    -1.60   0.109     -.008058     .000808
    atopally |   .0533455   .0369115     1.45   0.148    -.0189996    .1256906
    cntgdumy |      1.092    .038742    28.19   0.000     1.016067    1.167933
    bothmajr |  -.2319349   .1117437    -2.08   0.038    -.4509487   -.0129212
    onemajor |   .2537976   .0442523     5.74   0.000     .1670647    .3405304
           t |  -.0461914   .0027321   -16.91   0.000    -.0515462   -.0408365
          t2 |   .0007481   .0000602    12.43   0.000     .0006301     .000866
          t3 |  -3.25e-06   3.53e-07    -9.21   0.000    -3.94e-06   -2.56e-06
       _cons |  -2.509399   .0847086   -29.62   0.000    -2.675425   -2.343373
------------------------------------------------------------------------------

. 
. 
. ** Examining fatal MIDs instead of all MIDs
. probit mzfatall backdown1 midsin10 capratio capsum demlo atopally cntgdumy bot
> hmajr onemajor t t2 t3, cluster(dyadid)

Iteration 0:   log pseudolikelihood = -6083.3389  
Iteration 1:   log pseudolikelihood = -4683.0037  
Iteration 2:   log pseudolikelihood = -4353.1258  
Iteration 3:   log pseudolikelihood = -4345.4395  
Iteration 4:   log pseudolikelihood = -4345.4066  
Iteration 5:   log pseudolikelihood = -4345.4066  

Probit regression                                 Number of obs   =    1187663
                                                  Wald chi2(12)   =    1885.66
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -4345.4066                 Pseudo R2       =     0.2857

                             (Std. Err. adjusted for 33255 clusters in dyadid)
------------------------------------------------------------------------------
             |               Robust
    mzfatall |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   backdown1 |   .2062983   .0777379     2.65   0.008     .0539348    .3586618
    midsin10 |   .0089941   .0018271     4.92   0.000     .0054131    .0125751
    capratio |  -.5262031   .1415539    -3.72   0.000    -.8036436   -.2487626
      capsum |   1.958112   .3911379     5.01   0.000     1.191496    2.724728
       demlo |   -.012075   .0030585    -3.95   0.000    -.0180694   -.0060805
    atopally |  -.0005223   .0444719    -0.01   0.991    -.0876856    .0866411
    cntgdumy |   1.175051   .0474995    24.74   0.000     1.081953    1.268148
    bothmajr |  -.2616389   .1131413    -2.31   0.021    -.4833918   -.0398859
    onemajor |    .151869   .0673709     2.25   0.024     .0198245    .2839135
           t |  -.0547163   .0044472   -12.30   0.000    -.0634326       -.046
          t2 |   .0008893   .0001091     8.15   0.000     .0006754    .0011032
          t3 |  -3.94e-06   6.99e-07    -5.63   0.000    -5.31e-06   -2.57e-06
       _cons |  -2.841991   .1207741   -23.53   0.000    -3.078704   -2.605278
------------------------------------------------------------------------------
Note: 787 failures and 0 successes completely determined.

. 
. log close
      name:  <unnamed>
       log:  /Users/alexweisiger/Documents/research/Keren/Dataverse/results.log
  log type:  text
 closed on:  19 Jan 2014, 14:31:59
--------------------------------------------------------------------------------
